An algorithm for distributed parameter estimation in modal regression models
Xuejun Ma and
Xiaochao Xia
Statistical Theory and Related Fields, 2025, vol. 9, issue 2, 101-123
Abstract:
In this paper, we propose a new algorithm to handle massive data sets, which are modelled by modal regression models. Differing from the existing methods regarding distributed modal regression, the proposed method combines the divide-and-conquer idea and a linear approximation algorithm. It is computationally fast and statistically efficient to implement. Theoretical analysis for the resultant distributed estimator under some regularity conditions is presented. Simulation studies are conducted to assess the effectiveness and flexibility of the proposed method with a finite sample size. Finally, an empirical application to the chemical sensors data is analysed for further illustration.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2025.2483553 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tstfxx:v:9:y:2025:i:2:p:101-123
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20
DOI: 10.1080/24754269.2025.2483553
Access Statistics for this article
Statistical Theory and Related Fields is currently edited by Zhao Wei
More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().